And you would think it’s the boomers and Gen X. The generations sitting in the armchairs and corner offices are the ones who lie the most, right? By a planetary distance, it’s Gen Z.

Now, we are not just less nice, but also very pragmatic. And, to be honest, old. We’ve seen a lot. So we assume innovation cycles are called cycles because they repeat. The one we keep coming back to here is the late-90s digitalization cycle. You know, when everything moved from paper to the cloud. There was a thing called the Internet, and everyone wanted to have something online.

What happened there? Jimmy, the 60-year-old boss, who’d seen a computer at a tech expo once but still ran his business through his Rolodex and his secretary, and who was an avid fan of Dale Carnegie self-help books, saw a blue ocean opportunity. He hired Richie, a 25-year-old his cousin recommended with these exact words: “Richie is very good with computers.” What could go wrong? Richie had no idea about business goals, and “very good with computers” meant he was great at copying files between two external drives and had once coded a snake game in Turbo Pascal. But he was amazing at talking his way into situations, so he quickly became VP of Tech at Jimmy’s company.

I’m sure we all know these stories by heart. I personally had two bosses like Jimmy, and I was Richie at least three times in my career. One of my Jimmys even quoted Dale Carnegie to me, multiple times, to my utter amusement.

But I was able to solve the problems that came up later. Even though I talked my way in (a nice way to say I lied), I was creative and driven, and I had to solve the issues. Otherwise, my fakery would have been exposed. In almost every case, I did solve them. When there is pressure, you can learn. And marketing is not brain surgery, so it is easy to learn. So we don’t think faking AI skills will be the problem. People will adapt, regardless of their skills. If they don’t, they will be cut out of the workflows (a nice way to say unemployable).

What we do think is that AI readiness reports and company skills and competency audits have almost no real value. Honestly, we never thought they had in the first place. Any audit is a waste of money unless it is connected to action and execution. Most of these “AI readiness audit” services were just moneybags for consulting and wasted expenses for management, letting them sleep on the problem.

Now we have a very solid reason why they mean nothing. The baseline data is fake. Most people lie about their AI skills. They either lie consciously, to keep or get their jobs, or they lie without even realizing it. The simplest example of the latter is the stupidest question on these audits: have you used AI before? They say yes, and the company reports, “95% of our staff have used AI, we have no problem.” And the usage means a free version of ChatGPT, where they chat about something of elementary complexity.

What’s the takeaway? You can see from this week’s news that AI will own the entire marketing cycle, because it will own the entire decision cycle for the customer. Which means advanced, real AI skills will matter. And many people lie about their skill levels. So the solution is to care zero and jump straight into action. Train the staff without a prerequisite audit, and not on generic AI skills, but on very narrow, niche-focused ones. Skills that matter to YOU and your business. Then let them build the workflows around it. It is all action, execution, going deep and vertical rather than shallow and horizontal.

And you have to do it now. This is hour zero.

— Torsten and Peter

Workers Are Quietly Faking Their AI Skills

Turns out the “AI-native generation” might not be quite as AI-native as LinkedIn would have you believe.

A new GCheck report found that 63% of workers admit to exaggerating their AI capabilities, with the number jumping to a staggering 80% among Gen Z respondents. Many workers say they confidently discuss AI in meetings despite lacking hands-on experience. Some even volunteer for AI-related projects they are not qualified to handle. In other words: the AI skills bubble has officially entered the workplace.

The interesting part is not the dishonesty itself. It’s the psychology underneath it. Employees increasingly feel trapped between two fears: looking obsolete if they don’t embrace AI, and becoming replaceable if they do.

This creates a strange workplace dynamic where everyone publicly champions AI while privately resenting or avoiding it.

One CEO quoted in the report called this “double distortion” — workers overstating their AI fluency while quietly undermining adoption internally.

As a result many organizations may be massively overestimating their actual AI readiness.

If employees are bluffing their way through AI conversations, then internal surveys, transformation initiatives, and even hiring pipelines may be giving management a dangerously distorted picture of capability. AI adoption is increasingly becoming a culture problem disguised as a tooling problem.

Google Wants Ads to Feel Like Part of the Conversation

Google’s AI Mode is slowly turning into something closer to a conversational operating system for commerce — and ads are becoming part of the dialogue itself.

At I/O, Google revealed new Gemini-powered ad formats for AI Mode, including conversational sponsored responses and “Highlighted Answers” embedded directly into AI-generated recommendations. Instead of banner-style placements sitting beside search results, the ad becomes part of the generated answer. Ask how to make your house smell like a luxury spa, and Gemini may recommend products inline while explaining why they fit your needs.

This is a very different mental model from traditional search advertising. Historically, Google sold clicks around intent. AI Mode aims to monetize the intent-resolution process itself. The recommendation engine, explanation layer, and shopping journey are collapsing into a single conversational experience. That has profound implications for marketers because persuasion increasingly shifts from keywords to contextual reasoning.

The risk, of course, is that brands lose visibility into why they are being recommended in the first place. As AI interfaces become gatekeepers for discovery, marketers may gain efficiency while simultaneously losing transparency. We are entering the era of “black box persuasion,” where optimization increasingly happens inside systems brands cannot fully inspect.

OpenAI Is Slowly Building a Real Ads Platform

Our months-long ChatGPT Ads saga continues.

Since the launch, advertisers complained about severe underdelivery, weak reporting, and inventory shortages so bad that some campaigns barely spent at all. But OpenAI has been shipping aggressively.

In recent weeks, the company added daily budgets, geo-targeting down to ZIP code level, expanded reporting, conversion tracking, CPC bidding, and dynamic CTA buttons like “Shop Now” and “Learn More.”

The direction is becoming clearer: ChatGPT ads are evolving away from “sponsored placements in a chatbot” into a proper intent-driven advertising ecosystem. And unlike social media, where users are often passive or distracted, ChatGPT conversations tend to happen during active decision-making moments. Several advertisers already report conversion efficiency approaching non-brand Google Search performance — which is remarkable considering how immature the platform still is.

What’s becoming increasingly obvious is that conversational advertising behaves differently from both search and social. The user is not browsing. They are thinking out loud. That creates a very unusual ad environment where relevance matters far more than interruption. The companies that learn how to participate naturally in those conversations — rather than simply inject messages into them — will likely dominate this new category.

Nike Is Treating AI Commerce Like a World Cup Moment

Nike is becoming one of the launch partners for Google’s new Universal Cart system inside Gemini and AI Mode, timed deliberately around the 2026 FIFA World Cup.

For the first time, a major global brand is treating “agentic commerce readiness” as a real commercial priority rather than an experimental side project. Consumers will be able to discover Nike products, assemble multi-item carts, and move toward checkout directly inside conversational AI experiences powered by Google.

The broader implication is that product catalogs are becoming marketing infrastructure. AI systems cannot recommend products effectively if feeds are incomplete, poorly structured, or missing machine-readable attributes. In the old internet, bad product data hurt SEO. In the AI-commerce era, it may make you effectively invisible.

Expect every retail executive to ask the same question over the next year: “What’s our AI commerce strategy?” And increasingly, the answer will have less to do with creative campaigns and more to do with structured data pipelines, inventory synchronization, and conversational discoverability.

Spotify and Universal Just Made AI Music “Legitimate”

Spotify and Universal Music Group announced a landmark agreement that allows fans to create AI-generated covers and remixes of participating artists’ songs — with licensing, attribution, and revenue sharing built in.

This is a much bigger story than “Spotify adds an AI feature.” The music industry appears to be shifting from fighting generative AI toward containing and monetizing it.

Instead of trying to stop fan-made AI music entirely, Spotify and UMG are building a licensed ecosystem around it. Their argument is simple: AI-generated music is coming either way, so better to create a controlled system based on consent and compensation than let unlicensed “AI slop” dominate the market.

That framing is likely to spread far beyond music. We are starting to see the emergence of a broader compromise between creators and AI platforms: not full resistance, but negotiated participation. The industries that survive this transition best may not be the ones that reject generative AI outright, but the ones that figure out how to wrap ownership, attribution, and economics around it before someone else does.

One uncomfortable question, though, remains unresolved: if AI-generated creative output becomes effectively infinite, what happens to human attention? In a world where one song can become “10,000 versions,” discovery will become the real scarcity.

Share with a colleague. Earn paid membership months.

Refer a friend

Social media made that jump fifteen years ago. It started as a way to share photos and ended up rewiring elections, identity, and the news. AI is making the same jump right now, and this week made it obvious.

The clearest signal was the Monet story recently.

Someone posted a real Monet painting on X, labeled it AI-generated, and thousands of people lined up to explain why the “synthetic” brushwork and “soulless” composition gave it away. It was a real Monet. One part of this story is about how culturally uneducated people are these days, so that they can’t name a very famous artwork. But that’s just half of the story.

Research now backs up what that moment showed: people downgrade the exact same work the second they believe a machine touched it, even when they preferred it moments earlier. That is not an aesthetic judgment. It is a cultural one. Rejecting AI has become a way to signal taste, identity, and your side.

Marketers who pride themselves on trying the newest tools first now publicly claim they don’t use AI for copywriting. Even when they do. Most of them do. But “doing it manually” is the new European vacation, the new vintage shopping, or whatever the current cultural hobby of the upper-middle class is these days.

That is what a technology looks like when it crosses from tool to culture. The arguments stop being about whether it works and start being about what using it says about you.

— Torsten and Peter

PS: We, of course, are happy to admit that we use AI wherever we can. Not just because we don’t give a flying f about cultural norms (we don’t, never were), but because it just works, regardless of what humans think about it. And while craftsmanship has its beauty, it also has its limits.

1. The Monet Trap and Why the AI Backlash Is More About Culture Than Quality

A few days ago, someone posted a real Claude Monet painting on X, labeled it as “AI-generated,” and asked people to critique why it was inferior to a real Monet.

The replies were incredible. Thousands of people confidently dissected the “AI slop”: the reflections were “incoherent,” the composition lacked depth, the colors felt “soulless,” and the brushwork was “obviously synthetic.” One person reportedly wrote an 800-word breakdown explaining why the fake Monet failed to capture Monet’s genius.

It was an actual Monet.

Studies now show people consistently downgrade art, writing, and music once they believe AI was involved, even when they preferred the exact same piece moments earlier without the label.

In other words: a large chunk of the current AI backlash is not aesthetic judgment. It’s social signaling. It’s identity. It’s zeitgeist.

That doesn’t mean the backlash is fake. It’s very real. But it also means the “humans will always instantly spot AI” narrative is getting shakier by the week.

And yes, some companies will absolutely misread this and conclude “people secretly love AI content.” Careful.

The real lesson is subtler: audiences increasingly judge context as much as output.

Which means the brands that win won’t be the ones hiding AI usage. They’ll be the ones framing it intelligently.

2. Google Just Redesigned the Search Box. Your Traffic Problem Is About to Get Much Worse.

Google killed one of the internet’s most important design conventions this week: the classic search box.

The new version is multimodal, conversational, expandable, and deeply AI-native. You can now throw PDFs, videos, images, and even browser tabs directly into Search. AI Overviews and AI Mode are merging into one continuous experience. The “ten blue links” era is ending right in front of us.

And frankly, this is terrible news for publishers and most websites.

Google’s new flow is designed to keep users inside Google for as long as possible. Ask a question, get a synthesized answer, ask follow-ups, refine the request, generate visualizations, maybe even spin up mini apps directly in Search. Fewer clicks. Fewer visits. Fewer opportunities for your carefully optimized landing page to matter.

The important shift here is behavioral. For 25 years, Google trained people to compress intent into keywords. Now it wants them to think out loud. Longer queries, richer context, ongoing conversations.

That changes SEO fundamentally because optimization shifts from keyword matching toward semantic usefulness and machine readability.

If your content strategy still revolves around “ranking for terms,” you’re already playing the old game. The question now is: can an AI system easily extract your expertise, your product positioning, your structured data, your narratives? If not, you risk becoming invisible while still technically ranking.

And yes, Google says these AI experiences “increase engagement.” They probably do. Just not for your site.

3. Gemini Omni Is Not Just a Video Model.

Last week, we teased Google’s new Omni model as “a new video model.”

That was technically true. It was also underselling the story quite badly.

Announced officially at Google I/O, Gemini Omni is Google’s first true any-to-any multimodal model. Text, image, audio, video in. Video, image, audio out. One system, one conversational editing surface, one model reasoning across all modalities simultaneously.

For years, AI creative workflows have looked like Frankenstein pipelines: one model for images, another for lip sync, another for motion, another for sound, another for editing. Omni is Google's attempt to unify all of that into a single foundation layer, where the model actually understands relationships between modalities rather than stitching outputs together afterward.

The examples are honestly absurd. Generate a claymation explainer about protein folding. Turn a real-world clip into retro-futuristic sci-fi. Edit a scene conversationally across multiple turns while preserving continuity and physics. Add synchronized music and voiceovers on the fly. It feels less like prompting and more like directing.

The enterprise implications are huge, especially for marketing and L&D teams drowning in asset requests. Training videos, localized ad variants, product explainers, sales collateral, internal onboarding, the list is long and comprehensive.

4. Google’s Universal Cart Is the Clearest Agentic Commerce Signal Yet

Everyone is talking about AI agents helping you shop. Google is one of the first companies actually wiring the infrastructure together.

The newly announced Universal Cart follows you across Search, Gemini, YouTube, and Gmail. Add products anywhere inside Google’s ecosystem, and they persist inside a single AI-native cart that monitors price drops, checks compatibility issues, tracks stock availability, surfaces loyalty perks, and eventually handles checkout.

In other words: Google wants the cart itself to become an intelligent layer.

And because this sits on top of Google Wallet, the system also supports payments, offers, BNPL options, loyalty programs, and, eventually, autonomous purchasing rules via AP2, Google’s new Agent Payments Protocol.

If you run ecommerce, the important part isn’t “AI shopping assistant.” It’s that Google is becoming the orchestration layer between customer intent and merchant fulfillment.

The merchant technically remains the merchant of record. But the customer relationship starts shifting toward whoever controls discovery, comparison, recommendation, and checkout orchestration. That’s a very different power dynamic.

We’ve been talking for months about “agent readiness.” This is what it actually looks like operationally.

5. Figma’s Design Agent Is Extremely Good (And Potentially Awful News for Junior Designers).

For the last two years, most “AI design tools” were basically image generators pretending to understand UI work.

Figma’s new Design Agent feels different.

The key insight is simple: the agent lives inside the actual file. It understands your components, variables, design tokens, naming conventions, libraries, and systems. Instead of generating random pretty screens in a separate chat window, it edits real production work directly on the canvas.

The agent can generate layout directions, remix flows, bulk-edit components, summarize feedback threads, apply spacing changes across massive files, convert designs to dark mode, rename variables, and iterate visually while you keep working. Less “magic AI generator.” More “extremely fast junior designer who never gets tired of cleanup work.”

Which is exactly why junior designers should probably pay attention.

The reality is that a huge amount of entry-level creative work is mechanical. Padding adjustments, variant swaps, documentation, asset resizing, and consistency cleanup. The Figma agent attacks that layer directly. Not concepting or taste, or strategic thinking. The repetitive production layer.

That doesn’t mean designers disappear, but the value shifts upward faster.

Share with a colleague. Earn paid membership months.

Refer a friend

Google added an “Agentic Browsing” category to Lighthouse, the audit tool most web teams already use. Android started letting Gemini build carts, book rides, and fill forms across apps from a power-button press. Stripe and American Express rolled out payment systems built specifically for AI agents, including an Amex liability framework for when agents make purchasing mistakes. And we finally got real numbers on how long it takes for a new page to appear as a citation in ChatGPT and Claude.

For marketers, this is the week the agentic web stopped being a conference panel and started being plumbing. Browser standards, mobile defaults, payment rails, citation timelines. The unglamorous parts of the stack that decide what actually becomes a requirement versus what stays a demo.

The practical question over the next few quarters is whether your site, product data, checkout flow, and content can be read and acted on by something that is not a person. Because increasingly, the first interaction a customer has with your brand will happen inside an assistant, and you will not be in the room. What represents you is your structured data, your page hierarchy, your domain authority, and whether an agent can complete a task on your site without getting stuck.

Infrastructure means this will be everywhere, all at once. It also means that you have to understand how it works. If you don’t know it, you can’t build on it. If you can’t build on it, you can’t use it. If you can’t use it, you can’t leverage it. If you can’t leverage it, others will, and well… bad luck for you.

It seems the world is ready for the agentic web. Are you?

— Torsten and Peter

PS: for the next few months, we will publish AI-Ready CMO once a week. This is a minimum schedule, but it gives us, as editors, more flexibility to provide you with additional insights when needed.

1. Google Is Quietly Standardizing the Agentic Web

Google is quietly preparing the internet for AI agents, and this might end up being more important than most flashy model launches.

Last week, developers spotted a brand-new “Agentic Browsing” category inside Google Lighthouse, the auditing tool basically every serious web team already uses. Until now, Lighthouse graded your website for humans: speed, accessibility, SEO. The new category asks a different question entirely: can an AI agent actually use your site?

Without going into the technical details, Google wants sites to expose possible actions agents can take, maintain valid llms.txt files (like robots.txt which you might be familiar with), and clean up accessibility layers so agents can reliably understand buttons, forms, and workflows. In other words: the browser is slowly becoming machine-readable infrastructure, not just a human interface.

This matters because standards in the agentic web have been messy and fragmented so far. Lots of demos, not much consensus. But once Google starts baking these ideas directly into Chrome, the field can solidify fast.

If you work in e-commerce, SaaS, travel, ticketing, or basically any workflow-heavy business, “agent readiness” is going to start sounding a lot like “mobile readiness” did fifteen years ago. Not optional. Just another thing your customers quietly expect to work.

Learn how to build your agentic team or company from our latest Teamless demo:

2. Android Is Turning Into an Agent Layer

Google is also making the previous story much more urgent by pushing Gemini directly into Android itself.

Instead of opening ChatGPT or Gemini as separate destinations, Android is becoming the agent layer. Long-press the power button and Gemini can build grocery carts, reserve parking, reorder food, book rides, summarize webpages, or fill out forms across apps.

Your customer increasingly won’t “browse” your website the way humans historically did. Their agent will compare products, fill forms, navigate checkout flows, and potentially make decisions before a human even sees your landing page. Just watch their video: it is eye-opening:

And yes, there are still rough edges. Privacy concerns are obvious. User trust will take time. Most people won’t hand over full autonomy overnight. But some companies will make the mistake of assuming that means this shift is slow. It won’t be.

Learn how to build your agentic workflow from our latest Teamless demo:

3. Stripe and Amex Are Building the Trust Layer for AI Commerce

One missing puzzle piece for agentic commerce has always been payments.

That’s changing too. Both Stripe and American Express rolled out systems that let AI agents securely transact on your behalf without exposing raw payment credentials. Stripe’s upgraded Link wallet can generate one-time-use cards or shared payment tokens for agents. American Express went even further with “Agent Purchase Protection,” where Amex explicitly backs certain agent-caused purchasing mistakes.

Read that again because it’s easy to miss how weird this is: financial infrastructure companies are now designing liability frameworks for autonomous software agents.

The interesting question for us is what happens when discovery, comparison, checkout, and even repurchasing get compressed into a mostly invisible machine-to-machine workflow. Brand still matters. Trust still matters. But the actual interaction surface may increasingly happen inside an assistant instead of on your homepage.

4. AI Citation Timelines Finally Have Real Numbers

There’s a surprisingly interesting new data point around AI search visibility.

Josh Blyskal from Profound published early research tracking how long it takes newly published pages to get cited inside systems like ChatGPT and Claude. Median time to first citation: 6.8 days. Seventy-five percent of pages got cited within about 19 days. Ninety percent within 37 days.

That’s interesting partly because it contradicts some earlier “AI indexing happens almost instantly” narratives floating around LinkedIn.

The likely reality is that there are two separate clocks: crawlability and retrieval. Once your content is fully indexed and visible, models can surface it fast. But getting into those discovery systems reliably still depends on old-fashioned fundamentals: crawlability, authority, clean site structure, semantic clarity, and whether your content is actually useful enough to retrieve for a real query.

So what should you actually do with this?

Use Lighthouse to make sure your site is readable for LLMs. Publish under trusted domains whenever possible. Structure pages clearly with concrete answers, strong headings, and clear signals of expertise. Remember, AI visibility is not some magical new discipline completely detached from good web publishing practices. Some companies are looking for secret “AEO hacks” while their sitemap is broken and half their product data lives inside PDFs.

5. Google’s New Video Model Might Follow the Nano Banana Playbook

Finally, something lighter: Google’s upcoming Gemini Omni video model is starting to leak ahead of I/O, and it looks really good:

Generation quality may not yet fully match the top frontier models, but editing and conversational control seem extremely strong. Users are reportedly remixing clips, swapping objects, rewriting scenes via chat, and iterating interactively rather than treating video generation as a one-shot trial-and-error prompt.

That’s probably the more important direction anyway. Most marketers do not actually need Hollywood-grade video generation every day. We need fast iteration, editing, adaptation, localization, resizing, versioning, and campaign tweaking without opening twelve different tools.

We switched heavily toward Nano Banana internally because it quietly became the most useful image workflow model, not necessarily the prettiest benchmark demo. Google may be running the exact same playbook for video now.

Share with a colleague. Earn paid membership months.

Refer a friend

Katie’s core argument: the moat for a lean DTC founder isn’t a bigger team or a bigger raise. It’s a stack of narrow agents — social listening across Reddit, TikTok, reviews, and surveys; a chief of staff that briefs her every morning; a demand-planning system that frees her merchandiser from spreadsheets — all wired to her specific business questions. Off-the-shelf tools answer generic questions well. They don’t answer hers. The unlock was deciding to stop paying for fragmented subscriptions and start designing the system she actually needed.

About Katie Blouin

Katie Blouin is co-founder and CEO of Larken, a maternity and postpartum apparel company best known for its patented all-in-one nursing and hands-free pumping bra. She launched Larken seven years ago with her former law school roommate after both struggled with the same pain point during their second pregnancies. The company has been bootstrapped from day one, has appeared on the Inc. 5000 list multiple years running, manufactures entirely in the U.S., and operates with two co-founders, an outside marketing agency, and a network of contractors — most of whom are mothers themselves. Before Larken, Katie spent time at McDonald’s and across other large CPG environments. She is based in Atlanta.

You can find Katie on LinkedIn, at katieblouin.com, or follow Larken at shoplarken.com and on Instagram at @larken_shop.

About Peter Benei

co-founded the daily intelligence platform for senior marketing leaders. Peter has been serving as a CMO, marketing leader, and consultant to high-growth B2B scaleups for the past 10+ years. He has a background in advertising, working with Fortune 500 brands.

Connect with Peter on LinkedIn or read his newsletter.

Top 10 Takeaways

1. Bootstrapping forces discipline that VC-backed founders never develop. Every dollar and every hour has to earn its place. That constraint is uncomfortable in year one and a structural advantage by year seven. The founders who stay lean longest tend to build the cleanest decision-making muscles.

2. An agency-and-contractor model gives you specialist access that a single hire can’t. When your needs change every six months, a fixed in-house marketer becomes either underused or overwhelmed. An agency stack lets you pull different levers at different times without HR overhead — and without having to fire someone when the strategy shifts.

3. A buyer doesn’t always want to inherit your team. Founders are taught that a strong team is an asset. For some acquirers, it is. For others, low overhead and absorbable revenue is more valuable. Lean is not a weakness in an exit conversation — it’s an option.

4. Off-the-shelf social listening tools are a starting point, not a finish line. Brandwatch and the rest answer the questions they were designed to answer. The questions a specific founder has about their category, channels, and reviews are different — and now buildable in an afternoon.

5. The custom-agent advantage compounds with data. Larken has seven years of customer reviews, survey responses, customer service emails, and channel-specific performance data. Pointing a custom agent at that corpus surfaces patterns no off-the-shelf tool can see, because no off-the-shelf tool has access to it.

6. AI is a redeployment lever, not a replacement lever. Katie’s demand planner used to be a buyer at Victoria’s Secret. Paying her to fill in spreadsheet formulas is a category error. The agent does the formulas. She does the judgment. The total cost stays the same. The output gets dramatically better.

7. Personal life is where most founders first unlock AI, before they do so at work. The shift from chatbot to autonomous agent clicked for Katie when she fed three school newsletters into Claude and got her family calendar populated automatically. The work use cases came after. If you haven’t had that moment yet, look for it in the messy, manual parts of your home life first.

8. You don’t need to be technical. You need to be willing to start. Katie is a former lawyer with no engineering background. Her builds happen in Base44 (vibe-coded super agents) and Claude. The barrier to entry isn’t skill anymore. It’s the decision to stop reading tool newsletters and to pick one workflow to automate.

9. A chief-of-staff agent is the highest-leverage first build for a founder. Email triage, meeting prep, calendar reconciliation, newsletter summarization — all running overnight, all surfaced as a single morning brief. It’s the closest thing to hiring an EA at a fraction of the cost, and it works as well for a founder’s family logistics as it does for the business.

10. Stop thinking in tools. Start thinking in workflows. Katie spent months testing fifteen different subscriptions before realizing the right question wasn’t “what tool does this.” It was “if no AI was involved in my day, where am I spending my time, and which of those pieces deserve human judgment?” The infrastructure follows from that question, not the other way around.

5 Things Worth a CMO’s Attention

1. The “no marketing team” model is no longer a constraint — it’s a strategy

Larken is a multi-million-dollar brand on the Inc. 5000 list with two part-time co-founders and zero full-time marketers. The work is carried out through an agency relationship that has matured into something closer to an embedded team, supplemented by contractors who are mostly customers themselves. This isn’t a story about being unable to afford headcount. It’s a deliberate structural choice that gives Katie access to a roster of specialists she could never have justified hiring individually.

So what: If you’re sitting on a marketing org chart with five mid-level generalists, ask whether you’d rebuild it the same way today. The agency-plus-contractor model used to be a step toward a “real” team. For lean B2C operators — and increasingly for B2B teams under cost pressure — it’s the destination, not the detour. The hidden cost in this model is the founder’s coordination time. The hidden benefit is access to talent you couldn’t otherwise afford.

Read more

But if you read Stripe’s actual job description closely, it’s probably one of the clearest signals yet for where marketing work is heading. Not because of the AI buzzwords. Because the role is fundamentally not about campaigns, copy, media buying, or even strategy in the traditional sense.

It’s about changing how other marketers operate.

If you missed the news: Stripe is hiring someone to embed directly with marketing teams and make “AI the default mode.” Their success metrics are fascinating. Not leads generated, not pipeline, not MQLs. The role is judged by how many workflows become permanently transformed and how many colleagues start tasks with AI tools first.

That’s a management consultant. A systems architect. An internal transformation coach. A workflow designer. Maybe even a therapist for anxious marketers. Just not really what most people still imagine when they hear the word “marketer.”

And yes, there’s an obvious cynical interpretation floating around online already: this person’s job is to teach marketers how to automate themselves out of relevance. Some of that fear is rational. We’ve already seen entry-level marketing roles getting squeezed while senior orchestration roles become more valuable. Stripe’s posting quietly reinforces that trend. They want someone with enough technical fluency to redesign workflows, but also enough social intelligence to convince humans to actually change their habits.

That second part matters more than most companies realize.

The hard problem with AI adoption was never generating text or images. The models are already good enough for a shocking amount of work. The hard part is organizational behavior. People cling to existing workflows like emotional support blankets and teams build rituals around inefficiency. Managers habitually confuse busyness with value. You can buy ChatGPT Enterprise licenses for everyone in the building and still end up with a company where AI usage means “occasionally summarizing meeting notes.”

Stripe seems to understand this. The “forward deployed” part is borrowed from Palantir’s playbook, where engineers embed directly into organizations to reshape operations from the inside. That model works because transformation rarely happens through memos or training videos. It happens when someone sits next to you and rewires how the work actually gets done.

We’re probably going to see a lot more of these strange hybrid roles over the next two years. Not prompt engineers. That already feels like a very 2023 job title. More like AI operators, workflow architects, embedded automation leads, internal AI coaches.

People whose main skill is translating between business problems, human psychology, and machine capability.

And honestly, some companies are going to hire these people and completely misunderstand the assignment anyway. They’ll treat AI adoption like a software rollout instead of a cultural shift. The result will be the same depressing corporate workshop you’ve already suffered through a hundred times before, except now with ChatGPT slides and a mandatory prompt library sitting untouched in Notion somewhere.

— Torsten and Peter

Share with a colleague. Earn paid membership months.

Refer a friend

Want to learn and browse our hub for AI marketing courses, recommended tools, guides, workshops, checklists, and frameworks? → Start learning at our Hub

Yesterday it shipped.

The beta of Ads Manager is now officially open to U.S. advertisers: register, add payment, set budgets, upload ads, run campaigns, watch performance. CPC bidding is in alongside the existing CPM. Finally, there’s a Conversions API and a pixel for measuring whatever counts as a conversion in your business: purchases, sign-ups, lead forms. The previous $50K test-spend floor is gone too.

Registrations are manually approved for now, so we can’t walk you through the actual UI yet. That’ll come in a day or two, once our account clears.

Before you get excited, the obvious caveat: ChatGPT’s CTR is rough.

Independent estimates put it at around 1.3% versus Google Search’s 29.2%. Which makes sense if you think about it: people in a conversation aren’t trying to leave it. But Criteo, which has been running ads through ChatGPT since March, says LLM-referred traffic converts at roughly 1.5x the rate of other channels. So fewer clicks, yes, but higher intent.

If you’ve got a small budget that you don’t mind treating as a tuition fee, then go ahead, register, wait for approval, and run a campaign. That’s exactly what we will do this week. If you can, pick a high-consideration category where the research phase is long: finance, travel, B2B software. Buy on CPC, add the pixel, and watch CPA like a hawk. Impressions and click counts won’t tell you much yet — the only number worth watching is what landed on the other side of the click.

Benchmarks barely exist yet, so whatever you measure is more useful as your own baseline than as a comparison to anyone else’s. And the platform’s moving — CPC has reportedly settled at $3-$5, and the agency and tech partner list keeps growing.

Either way, it’s a real channel now, and it is exciting. It doesn’t happen often that a whole new category opens up for advertisers. Today is that day.

— Torsten and Peter

Share with a colleague. Earn paid membership months.

Refer a friend

Want to learn and browse our hub for AI marketing courses, recommended tools, guides, workshops, checklists, and frameworks? → Start learning at our Hub

Amman’s core argument: the AI edge in agency work isn’t a single clever agent. It’s a team of narrow agents with a brain on top — and a willingness to rewrite the client’s brief before you ever hit run. The agencies that still treat AI as a faster version of the old workflow are the ones losing pitches to those rebuilding the workflow from scratch.

About Amman Badlani

Amman Badlani has spent 22 years in digital media and commerce — across paid search, SEO, programmatic, retail media, and shopper marketing. He started on the client side at Philips Electronics, ran his own consultancy for a decade, serving SMBs, and then moved into the agency world 13 years ago. He has led practices at Omnicom, GroupM, and WPP Media, working with brands including Ford, Unilever, Bose, and Church & Dwight. He launched Meridian Commerce Partners, a venture focused on AI-native commerce orchestration. He is based in Brooklyn, New York.

You can find Amman on LinkedIn or email him at amman (at) meridiancommercepartners (dot) com

.

About Peter Benei

co-founded the daily intelligence platform for senior marketing leaders. Peter has been serving as a CMO, marketing leader, and consultant to high-growth B2B scaleups for the past 10+ years. He has a background in advertising, working with Fortune 500 brands.

Connect with Peter on LinkedIn or read his newsletter.

Top 10 Takeaways

1. Adopting AI isn’t enough — the systems around it are the moat. “I’m using AI” was a credible answer in 2023. It isn’t anymore. The differentiator is the framework: how you structure agents, how data flows between them, and how outputs ladder back to a business question. Tools are a commodity. Workflow architecture is not.

2. The agency model is shifting from creative service to software. WPP and the rest aren’t pivoting because they want to — they’re pivoting because clients are demanding outcomes the old retainer-and-headcount model can’t deliver. The next decade of agency work looks more like productized orchestration than billable hours on a brief.

3. Black-box platform dashboards fail because clients still want to see inside. Google and Meta keep pitching the “trust the algorithm” model — give us the goal, we’ll handle the rest. Clients don’t buy it. They want transparency on where media shows up, what’s working, and what’s not. Any agentic system you build has to surface reasoning, not hide it.

4. No single platform sees the full picture, which is why orchestration is the opportunity. A CPG brand runs across TV, programmatic, retail media, search, shopper, influencer, and else. None of those silos sees the others. The agency that builds an agentic layer on top of all of them — pulling category sales, retailer performance, weather triggers, competitive moves into a single brain — does work that no platform can replicate.

5. The 20-agent team beats the one-agent generalist. Most marketers building with AI create a single agent that does too much. The right architecture is the inverse: many narrow specialists (category sales, retailer performance, search velocity, competitive moves) that talk to each other, with an orchestration brain on top translating the conversation into the language of a senior client.

6. Don’t push the brief down to every agent. Push it to the brain. If you narrow data collection at the agent level based on a half-baked client brief, you’ll miss the surprises that make the work valuable. Feed agents wide data; let the orchestration layer apply the brief. That’s how you find the insight the client didn’t know to ask for.

7. Briefs are starting points, not contracts. Clients often don’t know what they want — they know what they think they want. Your job isn’t to ignore the brief; it’s to rewrite it more precisely than they did, then go find the data that answers the question they actually have. The hand-holding is the value.

8. AI gives mid-market teams access to talent they couldn’t afford before. Big agencies used to staff Oxford-trained statisticians on the floor to find anomalies in performance data. Mid-market couldn’t. Now, an analyst persona inside an agentic system does the same pattern recognition at a fraction of the cost — and runs continuously, not in monthly cycles.

9. Hourly billing breaks the moment AI compresses the work. Updating 150 banner variants used to take a designer two hours. Now it takes minutes. If you’re billing the same way, you’re either lying or you’re losing the contract. The value shifts from execution time to hypothesis quality — what to test, what to read into the results, what to recommend next.

10. Build your own frameworks. Don’t just read other people’s. Amman reads eMarketer, Euromonitor, and YouGov — but treats them as inputs, not outputs. The actual practice is synthesizing what he reads into three or four personal frameworks he keeps refining as new information lands. The frameworks are the asset. The articles are the raw material.

5 Things Worth a CMO’s Attention

1. The agency model is bifurcating, and it’s happening faster than the org charts suggest

The WPP earnings story isn’t an isolated event — it’s the leading edge of a structural shift. The big networks are being asked to deliver software-grade orchestration on creative-service contracts, and the math doesn’t work. Smaller, more nimble agencies that build proprietary agentic stacks are starting to win pitches against firms ten times their size, because the work product is genuinely different.

Read more

Which is why CEO Brian Niccol’s announcement this week reads like an admission. After trying for two years to make operations “lean”, Starbucks is hiring back thousands of baristas and pulling back its Siren System automation rollout. They tried to automate the part that wasn’t actually broken.

Yes, this is an automation story more than an AI one. But the lesson translates exactly, which is why we’re spending today on it.

If you missed the news: Starbucks spent two years rolling out the Siren System, a suite of equipment built to streamline drink-making and let stores run leaner. North American same-store sales fell 1% last quarter. Margins have shrunk five quarters running. Removing labor under the assumption that equipment could offset it just didn’t work out. So they’re walking it back, with around 3,000 stores getting expanded staffing by year-end.

I’ve seen commentaries saying this proves AI and automation don’t work. They are wrong. See what the announcement is not about.

The back office still runs on AI. Demand forecasting, supply chain, equipment maintenance, a new Green Dot Assist tool that helps baristas answer questions in real time, plus a wider generative AI rollout planned for 2026.

What’s getting walked back is specifically the equipment that replaced the human moment with the customer. The barista asking your name and writing it on a cup is mildly annoying. It is also the entire product. Strip it, and you’re charging $7 for an utterly mediocre coffee, which is a much harder business.

This is the same call most marketers are about to face.

Branding? Don’t automate it. That’s identity and taste: the parts of your job a model genuinely cannot do, no matter how many brand guideline PDFs you upload.

Messaging? Mostly don’t. Voice is the part of your brand that’s hardest to fake and easiest to wreck.

Execution? Automate everything you can. Ad variants, reporting cadences, asset resizing, briefing decks, pixel tracking, the seventeen Slack messages you write every Friday explaining what your team shipped. Hand all of it over.

The pattern repeats across categories. Apple Store without the Genius Bar. A boutique hotel where the concierge is a chatbot. Disney with QR codes instead of cast members at the park. Each one looks great on a cost spreadsheet and bleeds customers in real life, because the human is the brand.

Some companies will get this exactly backwards anyway. The visible, expensive, customer-facing automation gets prioritized because the savings show up cleanly on a P&L.

Nobody gets promoted for fixing reporting workflows. And a year later, the investor letter starts to sound a lot like Niccol’s.

— Torsten and Peter

In Partnership with Am I on AI

Some things are moving your AI visibility. Most aren’t. Do you know the difference?

The brands winning in AI search right now aren’t doing more. They’re doing less, but in the right places.

One company went from 24% to 79% ChatGPT visibility in twelve months. Not by adding budget. By knowing exactly where to double down and cutting what wasn’t working.

That clarity starts with knowing where you stand today.

We’re opening 10 free 40-minute GEO Strategy Sessions exclusively for AI-Ready CMO readers this week. Co-founder led. No pitch. Just clarity on where your brand stands and where to focus next. Spots are limited.

Book a free strategy session (10 spots only)

Book a session

Share with a colleague. Earn paid membership months.

Refer a friend

Want to learn and browse our hub for AI marketing courses, recommended tools, guides, workshops, checklists, and frameworks? → Start learning at our Hub

Four companies that posted record earnings in the same quarter announced layoffs in the same month, citing AI in plain language. Oracle. Meta. Snap. Microsoft. Together, somewhere between forty and fifty thousand jobs, depending on which estimate of Oracle you trust. None of these companies was in trouble. Meta is spending up to $135 billion on AI infrastructure this year — nearly double last year — and removing 14,000 positions from the same plan to help fund it.

This is a substitution.

We spent the month watching the same pattern repeat in three different markets. Profitable companies turning headcount into AI capex. Software vendors are quietly becoming backends that AI calls. Search traffic moving into conversations that brands cannot show up in unless they are already cited.

The April report covers all three and is now live for paid members. This piece is the version for everyone else — long enough to be useful on its own, short enough to read on a Sunday morning.

We called the report The Substitution Quarter because that is what April did across labor, tools, and channels simultaneously.

The labor side.

The Writer / Workplace Intelligence survey landed on April 7, and the numbers do not require interpretation. Sixty percent of executives plan to lay off employees who refuse to adopt AI. Seventy-seven percent will not promote them. Twenty-nine percent of employees, meanwhile, admit they are actively sabotaging their company’s AI rollout — among Gen Z, that figure is forty-four percent. The same week, Andrew Ng coined the phrase “the marketing bottleneck” to describe the obvious mismatch between AI-native engineering teams shipping daily and marketing teams that still need three weeks and four meetings to publish the landing page.

We covered the org-design version of this in Marketing Is the New Bottleneck and the more uncomfortable version — Meta secretly logging keystrokes to train its agents — in The Way You Work IS Training Data For AI.

The structural reading is straightforward. The roles getting cut at Block, Snap, Meta, and Oracle are not random. They are concentrated in the layer of work that translates strategic direction into operational tasks and tracks progress between teams. That layer is what AI handles well. Senior judgment at the top is harder to automate. Pure execution at the bottom is harder to organize against.

The middle is where the substitution is happening, and this is where most current marketing director roles sit.

Spencer Stuart’s salary data still shows a 15–22% Director-level premium for documented AI deployment. April is the month when premium starts to compress — because the supply of leaders who can point to a deployed, measured workflow is about to grow fast.

Refer a subscriber and unlock paid membership for free.

Refer a friend

The tool side.

On April 27, Adobe made its entire Creative Cloud — fifty-plus pro-grade tools across Photoshop, Illustrator, Firefly, Premiere, Lightroom, InDesign, and more — available as a connector in Claude. The same week, Anthropic shipped the same announcement across Blender, Autodesk, Ableton, Splice, Affinity by Canva, SketchUp, and Resolume. You describe what you want. Claude decides which tool to use. The tool runs in the background.

Read more

The summer RFS goes one layer deeper and deserves a new acronym because the old one no longer means what it used to.

For 20 years, SaaS meant Software-as-a-Service. You paid for a subscription. You got access to a tool. Your team did the work inside that tool.

YC just funded the inversion: Service-as-a-Software. You pay a subscription. You get the outcome. Nobody on your team does the work — because the service itself is now the product. Plug-and-play workforce is the endgame.

The summer RFS describes the three layers that make this possible:

Layer 1 — Company Brain. Extract tribal knowledge from people’s heads, Slack threads, tickets, and old emails. Structure it. Keep it current. Make it executable. Any startup that builds solutions for a company brain will have a very fast exit to one of the enterprise giants.

Layer 2 — The AI Operating System. Wire that brain into the live systems. Every meeting recorded, every ticket tracked, every decision logged. The company stops running on memory and starts running as a closed loop. Say hello to automating half of what any HR, procurement, legal, or finance department does today.

Layer 3 — AI-Native Service Companies. Plug in an external provider that runs the boring 80% of work — insurance brokerage, accounting, compliance, healthcare admin, and yes, marketing operations. It’s not external, like a freelancer, a consultant, or an agency. It’s basically a workforce installed for and in your company. Plug-and-play resource.

These layers are built on top of each other, and they won’t work without perfecting the lower layers first. Read it as three separate categories, and it’s a research note. Read it as one stack, and it’s how Service-as-a-Software gets built.

In Partnership with Am I on AI

You don’t need another dashboard. You need to know what to do Monday morning.

Most visibility tools tell you what happened. That’s useful for reports. It’s not useful for decisions.

What actually moves the needle: knowing which specific activities are driving your AI visibility this week, and which ones are quietly wasting budget.

That’s what the audit gives you. Not a score to screenshot. A direction to move in.

Run your free AI visibility audit

The new contract

The old SaaS contract was simple. You wanted a new capability, say graphic design, drag-and-drop website building, data analytics, and so on, so you signed up for a tool that gave your people that capability. The entire workforce’s career depended on these tools: do you know this, can you work with this, how proficient are you with this?

The old contract was: we sell you a tool, you figure out how to use it. That’s why every SaaS purchase came with implementation costs, training budgets, and a CSM emailing you about adoption.

The new contract has nothing to do with capabilities. It’s only about outcomes. The moment your company has a brain and your workflows are scanned and already partially automated internally, you lose something that made you unique. That pesky and ghastly institutional knowledge, which made everyone feel that their company and work are so unique. It’s not. Almost none of it is unique.

The new contract: we sell you a service that is fully adapted to your company and workflows, and it is mostly run on software (AI). We will send the people who will install this new service for you. All you need to do is pay the subscription and provide the access points for the install.

The reason this couldn’t happen before isn’t that the models weren’t good enough. They were. The reason was that no outside provider could understand your business well enough to deliver real work without your team holding their hand.

Layers 1 and 2 fix that. Once your context is extracted and live, an outside provider can execute against it cleanly. The handholding goes away. The service becomes the software.

The new marketing team

For marketing, the order of operations is:

1. Someone maps how marketing decisions actually get made at your company.

2. That map gets wired into your tools and stays up to date.

3. An external provider plugs in and runs the production layer — campaign briefs, copy variants, asset production, performance reporting — at a fraction of current cost.

Your agency stops being a strategic partner. Your martech vendor stops being a tool. Both become services you subscribe to and outputs you consume. The line between “agency” and “software” disappears, because the agency is the software.

This is also why this week’s Adobe story matters. When Adobe became a backend to Claude, it stopped being software in the old sense. It started being a backend service called via API. Same pattern, different layer of the stack. Service-as-a-Software is the direction of travel for everything in your category.

And on that unique workflow of yours? Super cute, but it is going to become training data. Literally every company is building that, right now. We wrote about what Meta is doing to their own employees. It’s just the most visible example among thousands of others.

The company brain solution with an operational layer is coming. Probably at the end of this year, early next year. Once those two layers are done, the rest is pretty simple and market-driven, so it will happen almost overnight.

Most marketing leaders have their wake-up call when their C-Suite tells them it is time to let go of 80% of their team, because there is this new company that does everything the marketing team does for 1/100 of the payroll costs in expense-ready subscription tokens.

Don’t be that marketing leader.

— Torsten and Peter

Share with a colleague. Earn paid membership months.

Refer a friend

And that’s exactly the point.

If you missed the news: Snap launched a new ad format that puts branded AI agents directly into the Chat tab. They show up as promoted snaps with a small “Ad” label, you tap in, and you can ask the agent things about products, prices, recommendations, whatever the brand has briefed it to handle. With Experian, that means credit scores, loans, and “exploring credit cards.” The format builds on regular Sponsored Snaps, which Snap says drive 22% more conversions and a roughly 20% lower cost per action — though those numbers belong to the existing format, not the new AI version. The AI version is still alpha with one partner.

The launch partner is the actual story. Functionally, this format is a sales rep with infinite patience for the boring questions you’d never ask a human one.

Credit bureaus sit in a high-consideration, information-heavy category, the kind where customers come in with real questions a chatbot can actually answer. Same goes for travel, real estate, insurance, healthcare, and most of B2B. Anything where the buyer journey involves clarifying. For impulse buys and brand awareness plays, this is going to feel forced. Nobody wants to chat with a Doritos bot 🤷‍♂️

Our geriatric readers might remember that Meta tried roughly this thing in 2016 with branded chatbots in Messenger. Tempting to say it was a spectacular flop, but it wasn’t. It was a quiet, whimper-flop. Snap is betting two things have changed: the AI is actually useful now, and the audience has already crossed the bridge (over half a billion people have messaged My AI). That’s not nothing, but the failure mode hasn’t really evolved. If every brand floods into Chat at once, you’re not engaging with sponsored agents, you’re scrolling past them.

And as Engadget pointed out: why ask Experian’s bot about credit when you could ask Claude or Gemini the same question and get an answer that isn’t trying to sell you anything (or at least not yet)?

The honest answer is distribution.

Snap has nearly a billion monthly users, and Experian has roughly zero of them sitting in a tab researching APRs. The format works because the brand shows up in the moment a question might form. The agent doesn’t have to beat Claude or Gemini at general knowledge. It just has to be the one that’s there. That’s a real edge, at least until users get sophisticated about it. Sponsored bots will eventually develop the same trust discount as sponsored posts.

In Partnership with Am I on AI

When everyone owns GEO, nobody owns GEO.

Here’s what we see constantly. The SEO team thinks it’s a content problem. The content team thinks it’s a PR problem. The PR team thinks it’s an SEO problem.

Meanwhile your competitors are being recommended by ChatGPT instead of you.

The fix isn’t a new hire. It’s giving every team a number they own — and giving yourself the view across all of them.

It starts with an audit.

Run your free AI visibility audit

For creative teams, this is the more interesting shift.

Every ad format until now has been something the user watches or clicks. This one, the user shapes in real time.

The deliverable is no longer a 15-second script. It’s a knowledge base. Tone guardrails. Fallback responses. A conversation tree that has to hold up when someone asks something stupid, off-topic, or weirdly specific. It’s a personality. Copywriters are about to become character designers, and most agencies don’t have a clear answer for who owns that work yet.

This works for Experian because Experian has a genuinely useful conversation to offer. The next ten brands won’t all clear that bar. A few will brief genuinely good agents. The rest are basically going to ship a slightly chattier banner ad. We’re going to see a lot of bad ones before we see the good ones — and they’re going to land in your DMs.

— Torsten and Peter

Share with a colleague. Earn paid membership months.

Refer a friend

Want to learn and browse our hub for AI marketing courses, recommended tools, guides, workshops, checklists, and frameworks? → Start learning at our Hub

The most oldschool behemoth. The Microsoft of the creative industry. The Big Contradiction of selling creative tools while building the least intuitive, least creative UX in the category. And yet, Adobe just did something genuinely innovative.

Adobe is now available on Claude as a connector, which means you can use Adobe products directly in Claude. It’s called Adobe Creativity and is available today. This means 50 different Adobe tools that you can use through AI. The implications are insane.

The workflow cannot be simpler. You prompt Claude. Claude decides which Adobe tool to use. Adobe runs in the background. And since this is AI, it all happens in about a minute.

The following will never happen again:

• Open Adobe. Wait five minutes for it to boot.

• Hunt for your files in Creative Cloud.

• Open the project in the specific tool. Wait again.

• Resize one image. Save. Export. Wait. Repeat 149 times.

That’s the simple stuff. The bigger projects take days and weeks. None of it had to.

Note: Stock prices took a nosedive, and their leaders said on the earnings call that the classic stock-image era is over and they have to adapt. So we’re genuinely not sure whether Adobe just made a bold strategic bet or quietly handed the keys to a competitor and called it innovation. In other words: we are not sure if they are brave first-movers, or just outright idiots to be honest.

The future of software is backend

Adobe’s tooling just became a backend for AI workflows. It’s an API call for Claude. No specific UX. No app icon to open. No update prompts. It sits there, quiet, called when needed.

It’s literally called a connector. The future of software is plug-and-play AI capabilities. You hook it up once, the AI knows how to use it, and you’ve upgraded your assistant with a new skill.

You’ve basically hired a junior designer. With a click of a button, for free.

In Partnership with Am I on AI

Six channels influence your AI visibility. You’re probably only watching one.

SEO, PR, content, social, partnerships, community. ChatGPT pulls from all of them when forming an opinion about your brand.

Your SEO team sees their slice. Your PR team sees theirs. Nobody has the full picture.

That’s not a team problem. That’s an intelligence problem.

See the full picture in one free audit.

Run your free AI visibility audit

We’ve run an experiment

Peter here. My wife, Sophie, is a designer. We live and breathe Photoshop and InDesign at home, so if you are a designer or work with designers, I feel your pain. I’ve watched Sophie yell at her screen to Photoshop. I’ve seen her face when a client asked her to resize 150 banner images. Adobe is a curse word in our home. Adobe is also borderline robbery on SaaS pricing, but that’s another story.

Sophie is skeptical of AI. She thinks creativity should stay human, forever, fair. But she also hopes the boring side of her job, the part she calls “pushing pixels on a screen,” gets handled by AI. The 150-banner resize jobs. They paid hundreds of euros. She’s on an hourly rate. Every minute felt like the dentist.

Last week, one of her clients sent her a brief. They make natural cereal bars and run a print catalog for wholesale buyers. The template never changes. Only the copy and product photos change with each new lineup. New lineup, new job: 30 image swaps and copy updates inside the existing template.

This is not creative work. This is pushing pixels. I’d argue this is 80% of any designer’s job, and it bills at a creative hourly rate. Sophie works fast and precisely. The client was happy. She shipped it the normal way.

Then I asked her to do it again. This time with Claude and the Adobe Creativity connector. The output was identical to the version she’d already shipped. Instead of hours, it took minutes.

Sophie is now wondering whether to schedule her client emails so the work looks like it took the usual time, or not send the file over too early. That arbitrage won’t last long. Everyone will figure this out fast. Until they do, it’s productivity heaven.

What it means

If 80% of a designer's time is pushing pixels, the same is true across your marketing stack. 80% of a copywriter's time is spent on reformatting. 80% of video editor time is spent on trimming. 80% of analyst time is spent reshaping data. That layer is now backend. The vendors that survive this become great APIs. The ones that don’t keep optimizing a UI nobody opens.

And the marketing leaders who survive it stop measuring their teams by output volume, and start measuring them by what only humans can still do.

— Torsten and Peter

Share with a colleague. Earn paid membership months.

Refer a friend

Want to learn and browse our hub for AI marketing courses, recommended tools, guides, workshops, checklists, and frameworks? → Start learning at our Hub

Andrew Ng (founder of Google Brain and AI legend) wrote about this last week, almost in passing. When AI-native engineering teams ship 10x or 100x faster, every adjacent function suddenly looks slow. He calls out marketing specifically — features built in a day, then weeks of scrambling to figure out how to communicate them. He even has a name for it:

The Marketing Bottleneck. That’s a bit of a pinch in the gut.

For most of the last twenty years, engineering took six months, marketing took six weeks, and nobody worried about who was slower because the math worked. The math doesn’t work anymore. If your eng team is shipping features faster than your CMS can publish a landing page for them, no amount of “we need a better campaign brief template” is going to fix it.

In Partnership with Am I on AI

Is your GEO budget allocated in the right places?

Most marketing leaders we talk to have started investing in GEO. Almost none of them know if it’s working.

Not because they’re not paying attention. Because they don’t have the right intelligence yet.

If you can’t answer “which of my channels is actually driving AI visibility?” - you’re running blind.

Run a free audit and find out where you actually stand.

Run your free AI visibility audit

Marketing isn’t slow because marketers are slow. We built it that way.

Brief goes to strategy. Strategy goes to creative. Creative goes to review. Review goes to the channel. Channel goes to analytics. Half a dozen people, a stack of meetings, and a Slack thread everyone tries to actively avoid. Each handoff was a fair tax when the rest of the company ran on the same clock. Now it’s the whole bill.

Cat Wu, who runs Claude Code at Anthropic, said something on Lenny’s podcast this week that I keep coming back to: “jobs are fake.” Her point is that if you understand the constraints and the goal, you can usually figure out what needs to happen and just do it — without waiting for someone with the right title to bless it. At Anthropic, PMs ship code, engineers write product specs, and designers do front-end. The roles blur because the work doesn’t care about the roles. The org chart is the bottleneck.

So what does an AI-native marketing team look like?

Smaller. Flatter. Heavy on generalists who can carry an idea from “I noticed this thing on Twitter” to a live landing page by Friday, without involving four other teams. The job titles still exist on LinkedIn. Inside the team, nobody really cares which one is yours.

If you’re a senior marketing leader, this is uncomfortable. A lot of what you’ve been promoted to do is run handoffs. You manage the strategy → creative → channel chain. You are the chain. Most of us are going to resist this for as long as we can, because the alternative is admitting that the team we built was sized for a tempo that no longer exists.

So what do you actually do about it this week?

First, pick one deliverable — a launch post, a brief, a landing page — and run it through one person end-to-end. No relay. Either quality drops, or you’ve been paying handoff tax for nothing.

Second, look at your recurring meetings and ask which ones exist only to coordinate handoffs. If you cancel the relay, the meeting goes with it.

Third, the next time you’re hiring or writing a promotion case, weight “can carry an idea to a live thing alone” higher than depth in a single specialty. Even if it makes the job description harder to write.

Yes, the teams that figure it out will be a lot smaller than the ones we run today. Some of us are going to be on the wrong side of that math. Pretending otherwise just delays the conversation.

— Torsten and Peter

Share with a colleague. Earn paid membership months.

Refer a friend

Want to learn and browse our hub for AI marketing courses, recommended tools, guides, workshops, checklists, and frameworks? → Start learning at our Hub

Meta says the data collected will not be used in performance reviews. Of course, they won’t. They also say they have safeguards for sensitive content. Of course, they have. Who wouldn’t believe Meta? But this dystopian surveillance-state-like extreme ignorance of personal privacy is just one side of the picture. And, of course, this could only happen in the US. Good luck doing this in Europe.

But we are not some geopolitical magazine, and we have a very extreme view on privacy as well. We also believe these things are kinda inevitable anyway, so let’s focus on what we can learn from this today.

In 2026, we started automating all knowledge work.

Because when something becomes training data for AI, it is just a matter of time before it gets automated.

Meta is reportedly spending up to $135 billion on AI infrastructure in 2026. They will lay off roughly 8,000 people and leave another 6,000 roles unfilled (this has already started in smaller batches). And in the middle of all that, they are paying engineers to generate workflow data by working. That tells you something specific: the bottleneck for the next generation of AI agents isn’t compute, isn’t model architecture, isn’t talent. It’s data. Specifically, agent-quality data. Examples of skilled humans completing real work on real software, in sequence, with judgment. That data does not exist at scale. So Meta is manufacturing it.

What marketers can learn from this

The companies that win the agent era won’t be the ones with the best tools. They’ll be the ones who own the best record of how their teams actually do the work.

Right now, your marketing team’s expertise lives in three places: the heads of your senior people, the half-finished SOPs in Notion, and the residue in Slack threads nobody can find. None of that is structured. None of it is reusable. None of it can train an agent to brief a campaign, screen a vendor, or write to your brand voice.

In twelve months, that’s going to be the difference between teams that scale and teams that get scaled. If you can hand a model a structured library of how your team plans a launch, qualifies a lead, or reviews creative, you compound. If you can’t, you’ll rent that capability from a vendor whose agents were trained on someone else’s data, at someone else’s price. Meta is doing the extreme version of this with surveillance.

You don’t need surveillance. You need intent. If you haven’t started documenting your workflows with extreme precision, today is the day to start.

What to do next

Three moves, in order, this quarter, starting today:

Read more

We can’t pinpoint the exact trigger, but we’ve started noticing that every leader we talk to is naming the same problems and proposing the same solutions for team-wide AI adoption in marketing. And we have also noticed that almost all of them were wrong.

They were wrong because they asked the wrong questions. And we all know that to make the right decisions, you first have to ask the right questions. Without some reflection, you can’t really provide a solution. This is true for individual problems and problems that affect entire teams.

This was the motivation behind writing about this topic. Helping you to make the right decisions by pointing you to ask the right questions first.

The classic cases of wrong questions

Before we jump in, let’s talk about the wrong questions. We want to name two. If you read any kind of online content around AI, you can probably guess what these are just by looking at what others are talking about.

1. A case against learning

The first thing is the obvious one. When your superior asks, "What’s our plan for AI adoption for marketing?” your answer points to your people. “We have initiated a learning program for our team members,” or something even more concrete, “We’ve bought X courses and enrolled in Y AI Academy, and we are going to the Z AI marketing conference.” You present the case as if it were a people problem, as it is a matter of “we don’t know enough.”

You are partially right. It’s somewhat a people problem, but it is not something that you can learn, but something you can experience. Your people won’t use AI more or better just because they read a guide, a manual, do a course, or master prompt engineering. There is no secret knowledge. No secret prompt. No hack, no tips, no hidden insight. No need to waste your precious time on consuming content on the best ChatGPT prompts for marketers.

What’s the good approach here? Simple. The solution is not learning but experiencing. By experimenting with AI, treating it as a colleague that you can work with, not a tool that you can wield to work, you can learn by doing. No prompt engineering course can teach you more than fixing your own prompts on real use cases, in real time, by you, based on your own creativity.

We don’t believe learning solves anything. The things that are kinda mandatory to learn about AI can be summed up in a 3- to 5-page article. From there, it’s all up to you to experiment.

Besides, let’s say you enroll in whatever I’m sure is a wonderful AI academy in May. The knowledge you’ll get is at best 2-3 months old. By the time you wrap up the course, the knowledge you’ve learned is already outdated. We know, just search our archives! We were questioning AI video capabilities half a year ago…

This is the reasoning behind our Knowledge Hub. It’s one of the richest, most fully loaded AI marketing knowledge dumps, with endless guides, manuals, courses, and workshops. And all of that content is free. Have a look, if you want to learn, be our guest. But don’t expect that consuming AI content for 2 weeks will get you further than experimenting and building something with AI for 2 hours.

2. The case against tools

Another classic CMO move is to throw resources at a problem you see.

I (Peter) was always so opposed to this that, when I got hired as a CMO, one of my very first moves was to audit the list of tools the team was using. I can tell you, it’s mindbendingly easy quick win. I always found thousands of dollars per month of worth of unused tools that my predecessors signed up for and forgot but still charged the company credit card. Astonishingly careless, really.

So when it comes to “what’s our AI strategy,” you might say, “oh, we’ve signed up for X tools and started to unroll another Y tool to our team with Z features.” The amount of time we get the “please recommend us the best tool for Y” is mind-blowing. Our response is always the same.

Like there is no secret prompt, hidden knowledge, or best course, there is also no almighty tool that exists. The solution is simple, again: go deep with one tool, possibly with some of the AI labs’ models. We, sadly, are not affiliated, but we recommend Claude. Set up custom projects there, use Cowork daily, and build something with Code. Instead of going deep horizontally with multiple tools, just sign your team members up to one tool, full access, unlimited credits, and good to go.

This is even more true than the knowledge problem, by the way. Almost all branded tools use the same AI models that AI labs develop, through their APIs. If you see a tool that does X, it’s probably a Claude/OpenAI or other labs’ API in a UX wrapper. Nothing wrong with it, of course, almost all modern CRM tools are basically cloud-based wrappers for Excel, but you have to keep in mind that the real differences between these tools are blurry. So it doesn’t mean much which one you sign up for.

If you are really into shopping, fine, we are all into giving what the people want. Our AI marketing tech stack list is the most comprehensive list on the internet. We list, review, compare, and even check whether the tools are right for your team. Browse the stack here. It is constantly updated.

No tools or courses will solve your AI adoption. Asking the right questions will.

Question 1

Where is AI pressure coming from?

We all know that you don’t just need to solve the problem. You also need to sell that solution internally. To do that, you need to know who you are selling to. Everything depends on the target audience, and your messaging should be tailored to them. This is a case in internal communications as well, obviously.

You have to know who’s driving the AI adoption conversation. There are 3 main possible drivers, and each requires a different story and messaging, therefore different solutions. There is a difference between “I want AI”, “We want AI”, and “We have AI.”

“I want AI” - The pressure is top-down. The management, the CEO, or whoever is in charge wants you to implement an AI adoption plan. The board, the investors, or the CEO themselves want AI because, well, no one knows why, but McKinsey told them it’s kinda important nowadays, and everyone seems to have AI, so why can’t we?

Addressing that means framing AI adoption as a story that serves as a use case, and attaching some goals and numbers to it. You don’t want to talk about tools or courses here. No one really cares. You want to talk about a use case you already did, expand on it with XYZ goals, and reach these goals by meeting XYZ numbers. All of this is framed into a story of change. Ideally, on a ppt/sheet that they can submit to the board as their own story…

“We want AI” - The pressure is bottom-up. Your team, employees, and colleagues are either already using AI in the background without a plan or want to use AI for their work.

Usually, it's the first. They are already using AI with their private licenses, without you knowing about it (shadow AI). Which is amazing, because your AI adoption has already happened on an individual level; your goal is to spread it evenly across the entire organization. In this case, you will need a governance plan, policies, and anything that formalizes, makes AI usage visible, and makes it manageable.

“We have AI” - The pressure comes from adjacent territories. The product or service you market has AI features and capabilities, or other teams than marketing are using AI team-wide, or sales is shipping outreach with AI-driven workflows.

Regardless of what the case is, the visibility is the same: if you don’t adopt AI, the marketing team seems like a slow horse. In this case, you need to have a plan on how your team can learn from other teams, collaborate on AI adoption, and probably surface AI usage that already happens in the background. Overcorrection shouldn’t feel like a catch-up, more like a sync.

Question 2

Which parts of your function will change first?

Not everything changes at the same speed. That’s the second trap. The marketing leaders we see move well in 2026 are the ones who can look at their org and, with a straight face, tell you which functions are about to get reshaped in the next two quarters, which will take a year, and which will still look mostly the same two years from now.

Here’s a rough starting point, though your specifics will differ:

The functions getting reshaped first are almost always the high-volume, pattern-heavy ones — inbound content operations, paid media iteration, lifecycle email, basic research, briefing workflows, first-draft copywriting. Not because humans can’t do them. Because the economics of having only humans do them stopped making sense about 6 months ago.

The ones on a twelve-month horizon tend to sit one layer up: creative strategy, brand planning, sales enablement, campaign orchestration. Agents are getting close, but the judgment density is still too high for full handoff.

The ones that look the same for a while yet are the judgment-and-relationship functions: partnerships, positioning, customer advisory, and category creation. Not forever. But for now.

A plan that doesn’t make this kind of distinction is just a wishlist. It treats a content operation and a brand strategy as if they’ll evolve on the same timeline. They won’t. You should know and point out the functions that will change first, so you can act accordingly.

Question 3

Where can agents expand?

This is the question that separates the leaders building something from the ones buying something. The default framing, the one your CEO has probably absorbed from whatever podcast they listen to, is that AI makes your team faster. That’s the shallow version. It’s also the version that quietly commoditizes your function.

The deeper framing is this: agents don’t just make your current work faster. They let you cover ground you couldn’t previously staff. That’s, if you ask us, a freakin’ fundamental difference. It’s the difference between your neighborhood hill and Mount Everest.

Examples? The competitor research you always wanted to run weekly, but could only afford quarterly. The account-level personalization your sales team has been asking for since 2021. The thirty-language version of your campaign that only the Fortune 100 could justify before. The customer-signal synthesis that once required a dedicated analyst now runs overnight.

The question isn’t “where can agents save us time.” It’s “where can agents let us do something we couldn’t do at all before.” Those are different plans. The first one defends a headcount reduction. The second one defends a growth story. Your CEO will take either one. The headcount reduction happens whether you have a say in it or not. The growth, well, that’s your job. Hint: You won’t get promoted for saving money on payroll.

Questin 4

Where does human judgment still matter most?

Read more

That doesn’t mean you should ignore it.

At my company, we’ve been living inside Claude Cowork for months now. I can tell you from experience that this always-on, shared-agent thing isn’t theoretical. I feel like I’ve hired five people who never sleep and live inside my chat window. It’s truly changing how I work and manage.

OpenAI’s version is architected differently enough that it’s worth understanding it, even if you can’t click the button yet.

Workspace Agents are essentially Codex (OpenAI’s coding agent) repackaged as a cloud service. It runs in the background. It maintains memory across sessions, connects to your Google Drive and Slack, and handles multi-step workflows without waiting for someone to open the app.

That’s the meaningful difference from Claude Cowork, which only works when the desktop app is running. OpenAI’s version is always online. You can schedule it to pull weekly metrics every Friday, route customer feedback from support into tickets for product, or research leads while you’re asleep.

But forget the tech specs for a second. The strategy is what matters.

For months, OpenAI looked like a company chasing shiny objects. Sora, a video-first social media platform. A separate ChatGPT for horny adults (for real). They were flailing. Then they killed both projects, tightened focus, and turned Codex into this.

And OpenAI has a wildcard Anthropic does not.

Read more

Kamil’s core argument: the AI edge isn’t at the frontier. It’s in the fundamentals. The companies winning with AI right now aren’t the ones with the best tools — they’re the ones where employees have the time, safety, and motivation to develop AI intuition on real work. Everything else is theater.

About Kamil Banc

is a fractional Chief AI Officer and AI culture advisor for mid-market American companies. He runs the AI Adopters Club, a newsletter with 13,000+ subscribers focused on practical AI workflows for operators, not enthusiasts. Born in Poland, raised in Germany, lived in Hawaii for a decade, now based in Jupiter, Florida. He describes himself, not entirely ironically, as an immigrant who’s more American than most Americans.

You can find Kamil at or on LinkedIn.

About Peter Benei

co-founded the daily intelligence platform for senior marketing leaders. Peter has been serving as a CMO, marketing leader, and consultant to high-growth B2B scaleups for the past 10+ years. He has a background in advertising, working with Fortune 500 brands.

Connect with Peter on LinkedIn or read his newsletter.

Top 10 Takeaways

1. Boring AI beats frontier AI. Chasing the latest model release is a distraction. The compounding returns come from building repeatable evaluation processes — how you prompt, how you test outputs, how you record what works across tools. That’s the infrastructure nobody brags about on X, and it’s the infrastructure that actually moves the needle.

2. AI is a culture problem, not a tool problem. Buying team licenses and declaring victory is how 90% of rollouts die. Adoption happens when leadership creates time, space, and psychological safety for experimentation — not when they hand down a mandate and a Chat GPT seat.

3. Shadow AI is the canary. Employees are already using AI. They just won’t tell you, because they’re worried that automating their work will automate them out of a job. No company has a real AI governance policy yet, and until there’s an onboarding path with no punishment, shadow AI stays in the shadows — and its productivity gains stay off the books.

4. There are three types of AI-adopting leaders. The honest cost-cutter (we want to do more with less). The optimist who becomes a cost-cutter two weeks into the assessment (we’ll enable our people, actually wait, let’s freeze hiring). And the true believer. All three need to tell employees the same thing: your current job is going away, so automate it yourself before someone else does.

5. The play is the director’s chair. If you’re a graphic designer, let AI push the pixels, and you steer the brand voice. If you’re a data analyst, let AI crunch the data, and you learn corporate storytelling. Get above the compute layer — where decisions happen before AI can make them — while the window is open.

6. Coordination is 30% of the job. AI is coming for that too. When leadership talks about “AI productivity gains,” they usually mean outputs. The bigger lever is coordination — the meetings, the status updates, the Slack back-and-forth. Reduce coordination by 10%, and you’ve freed up serious hours. You’ve also diplomatically reduced the need for stakeholders.

7. Jevons’ Law applies to labor. Give a worker 5x productivity, and the boss will give them 5x the work at the same pay — because the competition is doing it too. There’s no structural incentive for companies to give back the gains. Workers figuring this out are already running two or three full-time remote jobs and planning to retire in ten years.

8. Minds Blown Per Week is the real adoption metric. Corporate AI training — “here’s how to generate a report with AI” — doesn’t create adopters. What creates adopters is a personal moment of disbelief. Writing a children’s book in four hours. Building a tool for your kid. Once the mind is blown, the person starts asking, “What else can I do with this?” across every domain of their life. That’s the flywheel.

9. Horse blinders beat tool sprawl. For anyone starting out: pick one project, solve it end-to-end, ignore every new tool and model release until you’re done. The hype cycle is designed to keep you shopping. Strategic focus on a single problem teaches you more about how AI actually works than a year of newsletters.

10. Taste is the new bottleneck. When spinning up a landing page takes an hour instead of a week, the constraint shifts. The question stops being “can we build this?” and becomes “is it worth building?” That’s a judgment problem, not a capability problem — and most teams don’t have the organizational muscle for it yet.

Subscribe to AI-Ready CMO to catch future live episodes and get daily AI marketing intelligence that actually matters.

The article referenced on the live recording by Kamil: How to build your brand system in 45 minutes without an agency.

5 Things Worth a CMO’s Attention

Read more

They released ChatGPT Images 2.0, and the name does it no favors. “2.0” sounds like more of the same, but it isn’t. It’s an insane leap.

Take a look at the announcement page. OpenAI built the entire thing with the model: magazine spreads, newspaper layouts, cartoon panels, all with perfect typography and coherent visual logic. It’s the best product launch page I’ve seen this year, mostly because it proves the thing actually works under real design pressure:

As you probably realized, the headline fix is text.

AI image generators got better at it but still, often treated words like decorative mold. Images 2.0 renders dense copy, UI elements, iconography, and small labels accurately. At up to 2K resolution, with aspect ratios from 3:1 banners to 1:3 posters.

Finally, the model understands that graphic design is information arranged with intent. Pictures are just the container.

But if you’re paying for Plus or Pro, it gets better.

For those paying, the model goes further than mapping prompt to pixels. It searches the web, reasons through layout structure, and can generate up to eight related images in one go. Character continuity across comic panels. Campaign variations in different sizes. OpenAI calls this moving from “rendering to strategic design.” Fancy, but it works.

You can generate a whole storyboard, a localized ad set in five languages, or a product explainer sequence without stitching together twenty separate prompts.

There are rough edges (obviously). Wired tested Chinese text rendering and found plenty of fake characters dressed up to look like meme-poster writing. The model admitted as much. Non-Latin script support has improved (Japanese, Korean, Hindi, Bengali, an others), but if you’re running global campaigns, you still need a native speaker reviewing the output.

So what do you actually do with this?

Read more

You need a landing page by Thursday. Your design team is underwater, and you’re one more late-night Canva session away from a breakdown. So when Anthropic announced Claude Design this week, I was cautiously excited.

I’m happy to report that I was right: this feels like the ChatGPT moment for AI-led design. Seriously, just watch this video:

Claude Design is not an image toy. It’s a collaborative workspace (collaboration between you and Claude, that is). You describe what you need, and Claude builds working prototypes, pitch decks, and marketing collateral that actually respect your brand system. During onboarding, it reads your codebase and design files—your real fonts, your colors, your components—so you don’t get that generic AI slop. You refine through conversation, inline comments, or sliders that control spacing and layout live. When you’re happy, you export to Canva or PPTX. Or you hand the bundle to Claude Code and jump straight to production.

The quality is unlike anything we’ve seen so far.

The company Brilliant rebuilt complex pages that took 20 prompts elsewhere, using 2 prompts here. Datadog compressed a week of briefs and review rounds into one conversation. Figma’s stock cratered on the news (it didn’t help that Anthropic’s chief product officer resigned from Figma’s board the same day this launched).

But as you might expect, the product is not ready. Yet.

Early users are burning through their usage limits. One prolific tester spent sixty-five percent of his weekly allowance on a single landing page. Files vanish occasionally, and the UI has rough edges. Also, if your input is messy, Claude imports that mess with religious fidelity. This is still a research preview. Not a magic wand.

You have three moves.

Read more

Self-serve is the moment an ad platform stops being an experiment.

Google shipped it in 2000. Meta in 2007. Amazon a few years after. None of those three ever went back to managed-only. Once advertisers can log in, set a budget, and watch impressions tick up in real time, the platform is no longer something you negotiate access to, it’s something you use routinely.

The dashboard itself is reportedly basic. Reach campaigns only, CPM pricing, impressions and clicks in the reporting view. Clicks-as-pricing and conversion tracking are listed as “coming soon,” and OpenAI is also reportedly testing month-to-month deals at $30K–$50K instead of the original quarterly commitment. Translation: they want more advertisers in, faster, with less friction at every stage of the funnel from signing up to optimizing.

But infrastructure does not equal maturity. There’s no conversion pixel (for real). No live auction. No independent measurement layer. The case for paying $25–$60 CPM rests on a contextual argument that a ChatGPT user mid-conversation is closer to a high-intent search query than a passive social scroll.

Plausible. Probably true. Also, currently unverifiable.

So the picture is mixed but directionally clear. OpenAI has built more advertising infrastructure in nine weeks than most platforms manage in a year, and it’s pricing aggressively to pull buyers into the funnel before measurement catches up. The CPM compression alone — $60 to $25 in nine weeks — took Netflix’s ad tier closer to twelve months to deliver.

If you’ve been waiting for a sensible entry point, $50K with a self-serve dashboard is as close as you’re going to get for a while.

The advertisers experimenting now are paying for learning, not ROI, and they’ll have a year of platform-specific knowledge by the time CPC and conversion tracking arrive and the pricing reasserts itself. That asymmetry is the whole opportunity.

— Torsten and Peter

Share with a colleague. Earn paid membership months.

Refer a friend

Everything we know about AI marketing. Free.

Courses, workshops, frameworks, tool reviews, and implementation guides — all free, all on-demand. Built for senior marketers who don’t have time for theory.

→ Start Browsing The Hub

3 AI Marketing Tools To Try Today

Amplitude

→ Try it here

Amplitude is an AI-powered product analytics platform that tracks user behavior across web and mobile with cohort analysis and experimentation tools. Use it to get a crystal-clear view of what’s driving retention, conversion, and revenue beyond vanity metrics.

Lusha

→ Try it here

Lusha provides AI-powered B2B contact data with 280M+ verified contacts, buyer intent signals, and automated lead recommendations based on your ideal customer profile. Use it to skip the manual research grind and get accurate decision-maker emails and direct dials for outbound campaigns.

Reclaim AI

→ Try it here

Reclaim AI automatically schedules focus time, optimizes meetings, and protects work-life balance across Google Calendar and Outlook. Use it to reclaim 7+ hours per week by letting AI manage your calendar around actual priorities.

Paid members, your toolkit and this month’s report are below.

Read more